COST-SENSITIVE NEURAL ARCHITECTURES FOR HANDLING CLASS IMBALANCE IN HIGH-STAKES FRAUD DETECTION SYSTEMS
Keywords:
Fraud Detection, Class Imbalance, Cost-Sensitive Learning, Deep Neural NetworksAbstract
The rapid proliferation of digital financial transactions has necessitated the development of robust automated fraud detection systems. However, these systems face a persistent challenge: the class imbalance problem, where fraudulent activities constitute a negligible fraction of total transaction volume. Traditional neural network architectures, optimized for global accuracy, frequently fail to capture these rare events, leading to financially devastating false negatives. This study investigates the efficacy of Cost-Sensitive Neural Networks (CS-NN) designed to explicitly penalize the misclassification of the minority class. By integrating a weighted cost matrix into the backpropagation error function and leveraging historical optimization techniques, we propose a framework that prioritizes high-risk sensitivity without significantly degrading overall precision. We benchmark this approach against traditional algorithms, including Support Vector Machines and standard Deep Neural Networks, utilizing datasets with varying degrees of imbalance. Our results indicate that while traditional accuracy remains comparable across models, the proposed CS-NN architecture demonstrates a statistically significant improvement in recall and F1-scores for the fraud class. Furthermore, we explore the theoretical underpinnings of dropout and regularization in the context of imbalanced learning, suggesting that standard regularization techniques must be calibrated to prevent the suppression of rare signals. The findings suggest that incorporating domain-specific cost constraints directly into the learning objective offers a more viable path for high-stakes anomaly detection than post-hoc threshold adjustment or simple data resampling.
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